铂电阻测温非线性校正的两种新方法

根据正反馈原理，本文提出了铂电阻非线性校正基于级数展开和基于准相对误差最小的两种新方法，给出了参数设计的争有达式，设计及实际应用表明，这些方法简单、实用、有效，大大方便了铂电曙度传感器在测控系统中的应用。     

On a confidence interval about a parameter estimated by a ratio of normal random variables

A confidence interval is geometrically constructed about a parameter estimated by the ratio of bivariate normal random variables. The resulting confidence interval is equivalent to that of Fieller's theorem. The geometric construction shown that such intervals are conservative. Bioassay examples are used to demonstrate the technique.     

Rank tests in the two-sample scale problem with unequal and unknown locations

The two-sample scale problem is studied in the case of unequal and unknown location parameters. The method proposed is based on the idea of Moses (1963) and it is distribution-free. The two samples are separated into random subgroups of the same sizek. It is proposed to choosek=4 and to apply the Wilconxon test or the Savage test to the ranges or sample variances of the subgroups. The asymptotic power functions of the tests are compared. For small and moderate sample sizes simulations are carried out. Relations to some other procedures, especially to the method of Compagnone and Denker (1996) are briefly discussed.     

Single sampling plans indexed by point op control and inflection point

Three tables for selection of single sampling plans with any one of the following combinations of entry parameters are givens.

1) the indifference quality level and the average outgoing quality limit.

2) the indifference quality level with relative slope of the OC curve at that quality leve land.

3) the quality level corresponding to the inflection point with relative slope of the OC curve at that quality level.

A table enabling the transition from one set of parameters to match the OC curve of other similar sets is also given.     

试论罗素逻辑原子的两种简单性

有关罗素逻辑原子的简单性问题一直以来都存在着争议,这主要是由于他在不同时期不同本文中一些相互冲突、十分含糊的说辞造成的。文章将立足罗素与逻辑原子主义相关的一手文献,将简单分为两类：绝对简单和相对简单,分别探讨这些争论的焦点所在,并为罗素那些看似矛盾的说法给出自己的解释,也即为逻辑分析的方法正名。     

Consistency of the bootstrap for the ransformed two-sample t-test

A method of bootstrapping the two-sample t-test after a Box-Cox transformation is proposed. The procedure is shown to be consistent and asymptotically as efficient as the non-bootstrapped Box-Cox t-test. Because the bootstrap samples are drawn without the assumption of the same distributional shapes,the procedure may be more robust against violation of this assumption. Simulation results support this conjecture.     

Pseudo maximum likelihood estimation for the dirichlet-multinomial distribution

Pseudo maximum likelihood estimation (PML) for the Dirich-let-multinomial distribution is proposed and examined in this pa-per. The procedure is compared to that based on moments (MM) for its asymptotic relative efficiency (ARE) relative to the maximum likelihood estimate (ML). It is found that PML, requiring much less computational effort than ML and possessing considerably higher ARE than MM, constitutes a good compromise between ML and MM. PML is also found to have very high ARE when an estimate for the scale parameter in the Dirichlet-multinomial distribution is all that is needed.     

A Nonparametric Test for the Parallelism of Two First-Order Autoregressive Processes

A nonparametric testing procedure for the parallelism of two first-order autoregressive processes is presented. This paper discuss the Mann–Whitney statistic, its natural competitor two-sample t -test, and the bootstrap method. It studies the asymptotic efficacies of the studentized Mann–Whitney statistic and the t -test statistic with their relative efficiency. Simulation results for comparing the powers of these test statistics are also presented.     

DISTRIBUTION-FREE TESTS BASED ON SUB-SAMPLE EXTREMA FOR TESTING AGAINST POSITIVE DEPENDENCE

A class of distribution-free tests based on U-statistics has been proposed for testing the null hypothesis of independence against positive quadrant dependence. The tests are based on U-statistics and the Kendall's-tau test belongs to this class.     

PREDICTION OF THE FINITE POPULATION DISTRIBUTION FUNCTION UNDER GAUSSIAN SUPERPOPULATION MODELS

This article considers optimal prediction of the finite population distribution function under Gaussian superpopulation models, which allows auxiliary prior information to be incorporated into the estimation process. Large sample approximations for the variance of the optimal predictors are derived in some special important cases. A small scale Monte Carlo study illustrates comparisons between the optimal predictor and some others which are proposed in the literature. The conclusion is that the optimal predictor can be considerably more efficient in situations where the normal superpopulation model is adequate.